STOCK MARKET AGUMENTATION OF MACHINE LEARNING PREDICTION WITH APPLICATIONS

Stock market price prediction is a challenging task due to the highly volatile and dynamic nature of financial markets.Accurate forecasting of stock prices can assist investors in making informed trading decisions and minimizing financial risks.Traditional prediction methods often suffer from low accuracy as they fail to capture complex time-series patterns in stock price movements.This paper proposes a Stock Market Prediction System using Long Short-Term Memory (LSTM) neural networks.LSTM is well-suited for timeseries forecasting because it effectively learns long-term dependencies from historical stock price data.The system collects historical data from Yahoo Finance, preprocesses and normalizes the dataset, and trains an LSTM model to predict future stock prices.The predicted price is compared with the current market price to generate automated trading signals such as BUY, SELL, or HOLD.Furthermore, the system is deployed as a Flask-based web application, enabling users to input stock symbols and obtain prediction results through an interactive interface.Experimental results demonstrate improved prediction accuracy compared to traditional methods, highlighting the effectiveness of LSTM in financial forecasting.